def process_radardata(allradardatafilepathfolder,allradardatafilepath,chirp):
    
    # radardatafilepath=os.path.join(allradardatafilepathfolder,allradardatafilepath,allradardatafilepath+'.mat')    
    # mmWave_original_data=scio.loadmat(radardatafilepath) #解析从MATLAB中处理后得到的数据

    # mmwave_rx1=np.array(mmWave_original_data['RX1_data'])
    # mmwave_rx2=np.array(mmWave_original_data['RX2_data'])
    # mmwave_rx3=np.array(mmWave_original_data['RX3_data'])
    # mmwave_rx4=np.array(mmWave_original_data['RX4_data'])

    # radar_data=np.stack((mmwave_rx1,mmwave_rx2,mmwave_rx3,mmwave_rx4),axis=2)#shape为128*28800*4

    Capture_start_time = datetime.strptime(allradardatafilepath, '%Y%m%d%H%M%S')

    radardatalen=480000
    onefps=300/(radardatalen/chirp)
    framefilepath=os.path.join(allradardatafilepathfolder,allradardatafilepath,allradardatafilepath+'_frame.npy')
    # radararrayfilepath=os.path.join(allradardatafilepathfolder,allradardatafilepath,allradardatafilepath+'.npz')

    framelist=[]

    for i in range(int(radardatalen/chirp)):
        framelist.append(i*onefps+Capture_start_time.timestamp())

    framearray=np.array(framelist)
    np.save(framefilepath,framearray)